Environment-aware vehicle lane change prediction using a cumulative probability mapping model Online publication date: Mon, 05-Sep-2022
by Yongxuan Sun; Bowen Zhang; Zhizhong Ding; Momiao Zhou; Mingxi Geng; Xi Wu; Jie Li; Wei Sun
International Journal of Sensor Networks (IJSNET), Vol. 40, No. 1, 2022
Abstract: Vehicles' lane change is a major cause of serious traffic accidents. Therefore, it is imperative to implement an efficient and reliable lane change prediction and warning sub-system, for example in advanced driving assistance system (ADAS), to improve driving safety. Many approaches concerning the issue have been proposed so far. To have a good prediction performance, however, most of them require a large number of training data, and/or need radar or video equipment. Motivated by the demand in our development of vehicular ad-hoc network (VANET) terminals, a cumulative probability mapping (CPM) model for the prediction of lane change is proposed in this paper. The CPM model is constructed by taking into account both the vehicle motion information and the traffic context information that is acquired from VANET communications. The results based on the next generation simulation (NGSIM) dataset, which is made up of real vehicle trajectories, show that the accurate rate of the proposed method is 95.8%, and the false alarm rate is 11.6%. From the view of implementation, the proposed approach has the characteristics of low cost and low computational complexity, and can be used for embedded devices.
Online publication date: Mon, 05-Sep-2022
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